Update README.md

README.md

@@ -5,4 +5,75 @@ datasets:
 - Senqiao/VisionThink-Smart-Val
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
----
+---
+
+<p align="center" width="100%">
+<img src="https://raw.githubusercontent.com/dvlab-research/VisionThink/main/files/VisionThink.jpg" alt="Stanford-Alpaca" style="width: 100%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+
+# VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
+
+
+[![Paper](https://img.shields.io/badge/Paper-Arxiv%20Link-light)](https://arxiv.org/abs/2507.13348)
+[![HF](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Discussion-orange)](https://huggingface.co/papers/2507.13348)
+[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-yellow.svg)](https://github.com/dvlab-research/VisionThink/blob/main/LICENSE)
+<a href='https://huggingface.co/collections/Senqiao/visionthink-6878d839fae02a079c9c7bfe'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Data%20Model-Collection-red'></a>
+
+
+## Senqiao/VisionThink-Efficient
+
+This model is trained via reinforcement learning using [`Senqiao/VisionThink-Smart-Train`](https://huggingface.co/datasets/Senqiao/VisionThink-Smart-Train), demonstrating enhanced performance and efficiency on general VQA tasks.
+
+
+**VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning [[Paper](https://arxiv.org/abs/2507.13348)]** <br />
+[Senqiao Yang](https://scholar.google.com/citations?user=NcJc-RwAAAAJ),
+[Junyi Li](https://scholar.google.com/citations?hl=zh-CN&user=zQ0P3JAAAAAJ),
+[Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ),
+[Bei Yu](https://scholar.google.com/citations?user=tGneTm4AAAAJ),
+[Hengshuang Zhao](https://scholar.google.com/citations?user=4uE10I0AAAAJ),
+[Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ)<br />
+
+
+## Highlights
+<p align="center" width="80%">
+<img src="https://raw.githubusercontent.com/dvlab-research/VisionThink/main/files/Framework.jpg" alt="Stanford-Alpaca" style="width: 80%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+1. Our VisionThink leverages reinforcement learning to **autonomously** learn whether to reduce visual tokens. Compared to traditional efficient VLM approaches, our method achieves significant improvements on **fine-grained** benchmarks, such as those involving OCR-related tasks.
+
+2. VisionThink improves performance on **General VQA** tasks while reducing visual tokens by **50%**, achieving **102%** of the original model’s performance across nine benchmarks.
+
+3. VisionThink achieves strong performance and efficiency by simply resizing input images to reduce visual tokens. We hope this inspires further research into **Efficient Reasoning  Vision Language Models**.
+
+## Video
+<p align="center" width="85%">
+  <a href="https://www.youtube.com/watch?v=DGjbFbA5mBw" target="_blank">
+    <img src="https://raw.githubusercontent.com/dvlab-research/VisionThink/main/files/Video.png" alt="Stanford-Alpaca" style="width: 70%; min-width: 300px; display: block; margin: auto;">
+  </a>
+</p>
+
+
+
+## Citation
+
+If you find this project useful in your research, please consider citing:
+
+> This work is highly motivated by our previous effort on efficient VLMs, [**VisionZip**](https://github.com/dvlab-research/VisionZip), which explores token compression for faster inference. 
+
+```
+@article{yang2025visionthink,
+  title={VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning},
+  author={Yang, Senqiao and Li, Junyi and Lai, Xin and Yu, Bei and Zhao, Hengshuang and Jia, Jiaya},
+  journal={arXiv preprint arXiv:2507.13348},
+  year={2025}
+}
+@article{yang2024visionzip,
+  title={VisionZip: Longer is Better but Not Necessary in Vision Language Models},
+  author={Yang, Senqiao and Chen, Yukang and Tian, Zhuotao and Wang, Chengyao and Li, Jingyao and Yu, Bei and Jia, Jiaya},
+  journal={arXiv preprint arXiv:2412.04467},
+  year={2024}
+}
+```
+
+